Overview

Dataset statistics

Number of variables13
Number of observations51490
Missing cells0
Missing cells (%)0.0%
Duplicate rows424
Duplicate rows (%)0.8%
Total size in memory5.5 MiB
Average record size in memory112.0 B

Variable types

Numeric10
Categorical3

Alerts

Dataset has 424 (0.8%) duplicate rowsDuplicates
firstTower is highly overall correlated with firstDragonHigh correlation
firstDragon is highly overall correlated with firstTowerHigh correlation

Reproduction

Analysis started2023-03-12 10:13:59.390271
Analysis finished2023-03-12 10:14:21.815396
Duration22.43 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

t1_champ1win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.242138
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:21.958000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1787058
Coefficient of variation (CV)0.023460503
Kurtosis-0.014130646
Mean50.242138
Median Absolute Deviation (MAD)0.80666667
Skewness0.090030967
Sum2586967.7
Variance1.3893475
MonotonicityNot monotonic
2023-03-12T10:14:22.115760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1609
 
3.1%
49.26 1299
 
2.5%
49.56 1277
 
2.5%
48.88 1182
 
2.3%
50.52 1068
 
2.1%
51.63 1030
 
2.0%
49.95 1006
 
2.0%
51.04 988
 
1.9%
48.45 906
 
1.8%
50.83 880
 
1.7%
Other values (115) 40245
78.2%
ValueCountFrequency (%)
45.94 114
 
0.2%
46.67 146
 
0.3%
47.265 175
 
0.3%
47.67 125
 
0.2%
47.935 224
 
0.4%
48.05 155
 
0.3%
48.09 363
0.7%
48.13 613
1.2%
48.165 454
0.9%
48.365 63
 
0.1%
ValueCountFrequency (%)
53.01 234
 
0.5%
52.85 468
0.9%
52.815 182
 
0.4%
52.79 302
0.6%
52.75 191
 
0.4%
52.74 304
0.6%
52.51 137
 
0.3%
52.45 320
0.6%
52.31666667 98
 
0.2%
52.28 621
1.2%

t2_champ1win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.251476
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:22.276940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1788394
Coefficient of variation (CV)0.023458801
Kurtosis-0.049030983
Mean50.251476
Median Absolute Deviation (MAD)0.81
Skewness0.10262807
Sum2587448.5
Variance1.3896623
MonotonicityNot monotonic
2023-03-12T10:14:22.433617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1706
 
3.3%
49.56 1350
 
2.6%
49.26 1259
 
2.4%
48.88 1254
 
2.4%
50.52 1166
 
2.3%
51.63 1072
 
2.1%
51.04 973
 
1.9%
49.95 966
 
1.9%
50.83 900
 
1.7%
48.45 893
 
1.7%
Other values (115) 39951
77.6%
ValueCountFrequency (%)
45.94 115
 
0.2%
46.67 120
 
0.2%
47.265 141
 
0.3%
47.67 114
 
0.2%
47.935 204
 
0.4%
48.05 163
 
0.3%
48.09 367
0.7%
48.13 658
1.3%
48.165 446
0.9%
48.365 62
 
0.1%
ValueCountFrequency (%)
53.01 229
 
0.4%
52.85 519
1.0%
52.815 198
 
0.4%
52.79 298
0.6%
52.75 185
 
0.4%
52.74 326
0.6%
52.51 155
 
0.3%
52.45 326
0.6%
52.31666667 111
 
0.2%
52.28 607
1.2%

t1_champ2win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.258183
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:22.593298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.46
median50.095
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.58

Descriptive statistics

Standard deviation1.1835683
Coefficient of variation (CV)0.023549763
Kurtosis-0.054624727
Mean50.258183
Median Absolute Deviation (MAD)0.815
Skewness0.088369288
Sum2587793.8
Variance1.4008339
MonotonicityNot monotonic
2023-03-12T10:14:22.758029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1743
 
3.4%
48.88 1342
 
2.6%
49.26 1295
 
2.5%
50.52 1269
 
2.5%
49.56 1202
 
2.3%
51.63 1084
 
2.1%
51.04 1009
 
2.0%
49.95 996
 
1.9%
52.14 918
 
1.8%
48.45 902
 
1.8%
Other values (115) 39730
77.2%
ValueCountFrequency (%)
45.94 118
 
0.2%
46.67 115
 
0.2%
47.265 155
 
0.3%
47.67 128
 
0.2%
47.935 221
 
0.4%
48.05 159
 
0.3%
48.09 438
0.9%
48.13 643
1.2%
48.165 403
0.8%
48.365 59
 
0.1%
ValueCountFrequency (%)
53.01 239
 
0.5%
52.85 531
1.0%
52.815 188
 
0.4%
52.79 324
0.6%
52.75 175
 
0.3%
52.74 300
0.6%
52.51 154
 
0.3%
52.45 350
0.7%
52.31666667 93
 
0.2%
52.28 609
1.2%

t2_champ2win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.260276
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:22.917011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1881848
Coefficient of variation (CV)0.023640635
Kurtosis-0.12403565
Mean50.260276
Median Absolute Deviation (MAD)0.81
Skewness0.10945881
Sum2587901.6
Variance1.4117832
MonotonicityNot monotonic
2023-03-12T10:14:23.072736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1774
 
3.4%
48.88 1339
 
2.6%
49.26 1328
 
2.6%
50.52 1229
 
2.4%
49.56 1219
 
2.4%
51.63 1113
 
2.2%
51.04 997
 
1.9%
49.95 993
 
1.9%
48.45 962
 
1.9%
52.14 920
 
1.8%
Other values (115) 39616
76.9%
ValueCountFrequency (%)
45.94 87
 
0.2%
46.67 137
 
0.3%
47.265 169
 
0.3%
47.67 116
 
0.2%
47.935 206
 
0.4%
48.05 175
 
0.3%
48.09 437
0.8%
48.13 628
1.2%
48.165 409
0.8%
48.365 59
 
0.1%
ValueCountFrequency (%)
53.01 223
 
0.4%
52.85 597
1.2%
52.815 193
 
0.4%
52.79 346
0.7%
52.75 166
 
0.3%
52.74 336
0.7%
52.51 128
 
0.2%
52.45 329
0.6%
52.31666667 92
 
0.2%
52.28 612
1.2%

t1_champ3win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.272771
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:23.235074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.46
median50.1
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.58

Descriptive statistics

Standard deviation1.1813607
Coefficient of variation (CV)0.023499018
Kurtosis-0.10063439
Mean50.272771
Median Absolute Deviation (MAD)0.82
Skewness0.10369686
Sum2588545
Variance1.3956132
MonotonicityNot monotonic
2023-03-12T10:14:23.390943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1648
 
3.2%
48.88 1322
 
2.6%
49.26 1295
 
2.5%
50.52 1241
 
2.4%
49.56 1182
 
2.3%
51.63 1085
 
2.1%
51.04 1012
 
2.0%
49.95 979
 
1.9%
50.56 972
 
1.9%
48.45 959
 
1.9%
Other values (115) 39795
77.3%
ValueCountFrequency (%)
45.94 98
 
0.2%
46.67 115
 
0.2%
47.265 152
 
0.3%
47.67 116
 
0.2%
47.935 208
 
0.4%
48.05 156
 
0.3%
48.09 386
0.7%
48.13 612
1.2%
48.165 414
0.8%
48.365 48
 
0.1%
ValueCountFrequency (%)
53.01 262
0.5%
52.85 549
1.1%
52.815 179
 
0.3%
52.79 380
0.7%
52.75 167
 
0.3%
52.74 299
0.6%
52.51 157
 
0.3%
52.45 313
0.6%
52.31666667 97
 
0.2%
52.28 626
1.2%

t2_champ3win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.261329
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:23.560549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1872609
Coefficient of variation (CV)0.023621757
Kurtosis-0.10713562
Mean50.261329
Median Absolute Deviation (MAD)0.81
Skewness0.10656354
Sum2587955.8
Variance1.4095885
MonotonicityNot monotonic
2023-03-12T10:14:23.711828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1738
 
3.4%
48.88 1342
 
2.6%
49.26 1313
 
2.6%
50.52 1241
 
2.4%
49.56 1187
 
2.3%
51.63 1037
 
2.0%
51.04 1009
 
2.0%
49.95 933
 
1.8%
48.45 915
 
1.8%
52.14 903
 
1.8%
Other values (115) 39872
77.4%
ValueCountFrequency (%)
45.94 103
 
0.2%
46.67 120
 
0.2%
47.265 144
 
0.3%
47.67 117
 
0.2%
47.935 234
 
0.5%
48.05 162
 
0.3%
48.09 398
0.8%
48.13 648
1.3%
48.165 437
0.8%
48.365 59
 
0.1%
ValueCountFrequency (%)
53.01 245
 
0.5%
52.85 579
1.1%
52.815 205
 
0.4%
52.79 331
0.6%
52.75 154
 
0.3%
52.74 329
0.6%
52.51 153
 
0.3%
52.45 309
0.6%
52.31666667 116
 
0.2%
52.28 635
1.2%

t1_champ4win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.257608
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:23.875801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1852039
Coefficient of variation (CV)0.023582576
Kurtosis-0.12885603
Mean50.257608
Median Absolute Deviation (MAD)0.81
Skewness0.10587944
Sum2587764.2
Variance1.4047082
MonotonicityNot monotonic
2023-03-12T10:14:24.033660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1721
 
3.3%
49.26 1335
 
2.6%
48.88 1332
 
2.6%
50.52 1252
 
2.4%
49.56 1191
 
2.3%
51.63 1057
 
2.1%
51.04 969
 
1.9%
48.45 962
 
1.9%
49.95 930
 
1.8%
50.56 912
 
1.8%
Other values (115) 39829
77.4%
ValueCountFrequency (%)
45.94 98
 
0.2%
46.67 125
 
0.2%
47.265 137
 
0.3%
47.67 100
 
0.2%
47.935 214
 
0.4%
48.05 185
 
0.4%
48.09 430
0.8%
48.13 663
1.3%
48.165 414
0.8%
48.365 69
 
0.1%
ValueCountFrequency (%)
53.01 208
 
0.4%
52.85 545
1.1%
52.815 174
 
0.3%
52.79 338
0.7%
52.75 163
 
0.3%
52.74 343
0.7%
52.51 145
 
0.3%
52.45 347
0.7%
52.31666667 87
 
0.2%
52.28 620
1.2%

t2_champ4win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.263177
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:24.196200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.1
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1808585
Coefficient of variation (CV)0.023493512
Kurtosis-0.11545642
Mean50.263177
Median Absolute Deviation (MAD)0.82
Skewness0.10246827
Sum2588051
Variance1.3944269
MonotonicityNot monotonic
2023-03-12T10:14:24.346032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1823
 
3.5%
49.26 1348
 
2.6%
48.88 1302
 
2.5%
49.56 1263
 
2.5%
50.52 1263
 
2.5%
51.63 1090
 
2.1%
51.04 1019
 
2.0%
49.95 961
 
1.9%
48.45 866
 
1.7%
49.26 861
 
1.7%
Other values (115) 39694
77.1%
ValueCountFrequency (%)
45.94 91
 
0.2%
46.67 118
 
0.2%
47.265 165
 
0.3%
47.67 138
 
0.3%
47.935 199
 
0.4%
48.05 171
 
0.3%
48.09 386
0.7%
48.13 683
1.3%
48.165 393
0.8%
48.365 57
 
0.1%
ValueCountFrequency (%)
53.01 216
 
0.4%
52.85 550
1.1%
52.815 200
 
0.4%
52.79 343
0.7%
52.75 194
 
0.4%
52.74 271
0.5%
52.51 132
 
0.3%
52.45 349
0.7%
52.31666667 110
 
0.2%
52.28 613
1.2%

t1_champ5win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.245727
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:24.513357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1861035
Coefficient of variation (CV)0.023606056
Kurtosis-0.056787134
Mean50.245727
Median Absolute Deviation (MAD)0.81
Skewness0.10329965
Sum2587152.5
Variance1.4068414
MonotonicityNot monotonic
2023-03-12T10:14:24.667281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1613
 
3.1%
48.88 1287
 
2.5%
49.26 1261
 
2.4%
49.56 1251
 
2.4%
50.52 1086
 
2.1%
51.63 1055
 
2.0%
49.95 1011
 
2.0%
51.04 987
 
1.9%
50.56 932
 
1.8%
48.45 923
 
1.8%
Other values (115) 40084
77.8%
ValueCountFrequency (%)
45.94 114
 
0.2%
46.67 127
 
0.2%
47.265 169
 
0.3%
47.67 133
 
0.3%
47.935 215
 
0.4%
48.05 184
 
0.4%
48.09 364
0.7%
48.13 651
1.3%
48.165 427
0.8%
48.365 50
 
0.1%
ValueCountFrequency (%)
53.01 245
0.5%
52.85 524
1.0%
52.815 215
 
0.4%
52.79 292
0.6%
52.75 191
 
0.4%
52.74 340
0.7%
52.51 143
 
0.3%
52.45 297
0.6%
52.31666667 99
 
0.2%
52.28 595
1.2%

t2_champ5win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.25316
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.5 KiB
2023-03-12T10:14:24.841543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1859497
Coefficient of variation (CV)0.023599505
Kurtosis-0.075082007
Mean50.25316
Median Absolute Deviation (MAD)0.81
Skewness0.11012221
Sum2587535.2
Variance1.4064767
MonotonicityNot monotonic
2023-03-12T10:14:24.999965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1670
 
3.2%
48.88 1300
 
2.5%
49.56 1267
 
2.5%
49.26 1250
 
2.4%
50.52 1107
 
2.1%
49.95 1078
 
2.1%
51.04 1038
 
2.0%
51.63 1035
 
2.0%
48.45 900
 
1.7%
50.56 849
 
1.6%
Other values (115) 39996
77.7%
ValueCountFrequency (%)
45.94 94
 
0.2%
46.67 157
 
0.3%
47.265 162
 
0.3%
47.67 111
 
0.2%
47.935 232
 
0.5%
48.05 166
 
0.3%
48.09 394
0.8%
48.13 625
1.2%
48.165 407
0.8%
48.365 48
 
0.1%
ValueCountFrequency (%)
53.01 264
0.5%
52.85 567
1.1%
52.815 224
 
0.4%
52.79 273
0.5%
52.75 183
 
0.4%
52.74 313
0.6%
52.51 147
 
0.3%
52.45 337
0.7%
52.31666667 104
 
0.2%
52.28 606
1.2%

firstBlood
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size804.5 KiB
1
26113 
2
24822 
0
 
555

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51490
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 26113
50.7%
2 24822
48.2%
0 555
 
1.1%

Length

2023-03-12T10:14:25.144779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T10:14:25.276897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 26113
50.7%
2 24822
48.2%
0 555
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 26113
50.7%
2 24822
48.2%
0 555
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51490
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26113
50.7%
2 24822
48.2%
0 555
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 51490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26113
50.7%
2 24822
48.2%
0 555
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26113
50.7%
2 24822
48.2%
0 555
 
1.1%

firstTower
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size804.5 KiB
1
25861 
2
24416 
0
 
1213

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51490
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 25861
50.2%
2 24416
47.4%
0 1213
 
2.4%

Length

2023-03-12T10:14:25.379670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T10:14:25.497494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 25861
50.2%
2 24416
47.4%
0 1213
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 25861
50.2%
2 24416
47.4%
0 1213
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51490
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25861
50.2%
2 24416
47.4%
0 1213
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 51490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25861
50.2%
2 24416
47.4%
0 1213
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25861
50.2%
2 24416
47.4%
0 1213
 
2.4%

firstDragon
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size804.5 KiB
2
24800 
1
24690 
0
 
2000

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51490
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 24800
48.2%
1 24690
48.0%
0 2000
 
3.9%

Length

2023-03-12T10:14:25.613003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T10:14:25.736466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2 24800
48.2%
1 24690
48.0%
0 2000
 
3.9%

Most occurring characters

ValueCountFrequency (%)
2 24800
48.2%
1 24690
48.0%
0 2000
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51490
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 24800
48.2%
1 24690
48.0%
0 2000
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 51490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 24800
48.2%
1 24690
48.0%
0 2000
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 24800
48.2%
1 24690
48.0%
0 2000
 
3.9%

Interactions

2023-03-12T10:14:18.474632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:01.373078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:03.872005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:06.827518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:08.997084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:10.508461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:12.034169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:13.782831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:15.303006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:16.821167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:18.694973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:01.614082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:04.114487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:07.099251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:09.154723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:10.665720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:12.180888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:13.933532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:15.452665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:16.985645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:18.903803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:01.859273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:04.335234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:07.353987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:09.305779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:10.814840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:12.332673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:14.087982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:15.605970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:17.136328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:19.129135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:02.119725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:04.557967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:07.592163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:09.459360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:10.966947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:12.478116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:14.245780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:15.758724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:17.290604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:19.353098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:02.380297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:04.781589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:07.801315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:09.609090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:11.116670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:12.626858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:14.395342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:15.910450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:17.445931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:19.546917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:02.627805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:05.020659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:08.020177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:09.754407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:11.266056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:12.782164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:14.545598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:16.062163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:17.594045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:19.775653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:02.883368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:05.434866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:08.375740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:09.901293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:11.419764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:12.934880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:14.696105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:16.210919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:17.751071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:19.956022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:03.136479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:05.974502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:08.533206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:10.049581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:11.584063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:13.090999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:14.850843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:16.364526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:17.900287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:20.179526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:03.394907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:06.294241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:08.692054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:10.198144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:11.737957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:13.246996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:14.996040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:16.524183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:18.057293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:20.390704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:03.632843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:06.546501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:08.847179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:10.357710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:11.889243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:13.404682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:15.145618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:16.673755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:14:18.248304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-12T10:14:25.841473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
t1_champ1wint2_champ1wint1_champ2wint2_champ2wint1_champ3wint2_champ3wint1_champ4wint2_champ4wint1_champ5wint2_champ5winfirstBloodfirstTowerfirstDragon
t1_champ1win1.000-0.017-0.000-0.016-0.011-0.010-0.011-0.007-0.000-0.0110.0070.0120.005
t2_champ1win-0.0171.000-0.010-0.004-0.016-0.010-0.020-0.014-0.007-0.0150.0080.0130.000
t1_champ2win-0.000-0.0101.000-0.017-0.011-0.010-0.014-0.003-0.017-0.0110.0120.0170.011
t2_champ2win-0.016-0.004-0.0171.000-0.008-0.003-0.010-0.0110.002-0.0120.0120.0180.009
t1_champ3win-0.011-0.016-0.011-0.0081.000-0.010-0.009-0.009-0.001-0.0150.0000.0180.000
t2_champ3win-0.010-0.010-0.010-0.003-0.0101.000-0.015-0.013-0.006-0.0130.0140.0150.010
t1_champ4win-0.011-0.020-0.014-0.010-0.009-0.0151.000-0.012-0.001-0.0060.0000.0180.000
t2_champ4win-0.007-0.014-0.003-0.011-0.009-0.013-0.0121.000-0.018-0.0010.0080.0150.000
t1_champ5win-0.000-0.007-0.0170.002-0.001-0.006-0.001-0.0181.000-0.0150.0110.0160.009
t2_champ5win-0.011-0.015-0.011-0.012-0.015-0.013-0.006-0.001-0.0151.0000.0070.0110.000
firstBlood0.0070.0080.0120.0120.0000.0140.0000.0080.0110.0071.0000.4980.379
firstTower0.0120.0130.0170.0180.0180.0150.0180.0150.0160.0110.4981.0000.582
firstDragon0.0050.0000.0110.0090.0000.0100.0000.0000.0090.0000.3790.5821.000

Missing values

2023-03-12T10:14:20.711212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-12T10:14:21.484004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

t1_champ1wint2_champ1wint1_champ2wint2_champ2wint1_champ3wint2_champ3wint1_champ4wint2_champ4wint1_champ5wint2_champ5winfirstBloodfirstTowerfirstDragon
050.35049.19000050.2350.6752.79050.37049.74000049.7550.45048.88211
151.32051.85000051.5250.0950.46551.55050.47000049.7549.88548.88121
249.95051.85000050.1050.7352.85048.09050.37000049.7549.74048.88121
347.93551.32000049.6650.6752.45049.74049.56000049.7549.92048.88222
449.26049.28000052.8548.4549.74049.86052.31666749.7550.95048.88211
549.92051.63000051.3249.7448.13049.11049.97500049.7548.85048.88211
649.97549.26000049.7450.4750.84051.71049.26000049.7550.65048.88111
748.45050.45000050.6752.1449.76049.27549.26000049.7549.11048.88211
850.52049.97500048.5851.3251.63049.38048.45000049.7545.94048.88100
949.95051.43666750.2350.0950.10050.18049.28333349.7549.46048.88211
t1_champ1wint2_champ1wint1_champ2wint2_champ2wint1_champ3wint2_champ3wint1_champ4wint2_champ4wint1_champ5wint2_champ5winfirstBloodfirstTowerfirstDragon
5148049.3849.76052.75000050.0951.32049.35000050.84000051.83000050.42500050.47112
5148149.2650.67049.64500050.5249.92049.46666750.17000049.82000051.43666750.47121
5148250.8450.46549.64000050.9251.83051.87000050.52000049.82000051.32000050.47222
5148349.9548.96050.45000050.6749.97550.52000051.87000051.30000050.52000050.47122
5148452.2849.35049.83333351.8752.20051.04000051.03000049.11000050.10000050.47211
5148550.2350.84051.63000049.2850.53052.74000050.43000051.43666748.45000050.47211
5148650.5449.28049.26000052.8548.88049.62500048.67000048.16500050.83000050.47111
5148749.6850.52051.03000051.6348.16549.26000049.46666749.98500049.03000050.47111
5148850.3752.85049.28333349.2650.18048.16500051.32000050.95000050.23000050.47222
5148951.4350.92049.88500049.0349.28052.14000050.23000050.67000052.74000050.47112

Duplicate rows

Most frequently occurring

t1_champ1wint2_champ1wint1_champ2wint2_champ2wint1_champ3wint2_champ3wint1_champ4wint2_champ4wint1_champ5wint2_champ5winfirstBloodfirstTowerfirstDragon# duplicates
045.94049.6151.04050.81049.26048.58000052.75000050.1048.88049.3801223
2648.45050.5253.01050.67052.85048.96000049.88500051.0449.26049.6452113
4448.88049.7549.74048.45051.04051.43666751.32000051.5251.30049.2601113
5948.96051.0352.74050.43052.14049.68000051.04000051.4050.56049.5601223
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